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Using Ratios of Successive Returns for the Estimation of Serial Correlation in Return Series

Author

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  • Erhard Reschenhofer

    (Department of Statistics and Operations Research, University of Vienna, Austria)

Abstract

This paper proposes to estimate the first-order autocorrelation of asset returns by the rescaled sample mean of suitably transformed ratios of successive returns. The simplicity of this estimation method allows the monitoring of the stability of the estimates over time and the almost instantaneous detection of any structural break without any delay caused by an estimation window. In an empirical study of index returns, its use considerably increases the profitability of a simple trading strategy which switches between the index and cash.

Suggested Citation

  • Erhard Reschenhofer, 2017. "Using Ratios of Successive Returns for the Estimation of Serial Correlation in Return Series," Noble International Journal of Economics and Financial Research, Noble Academic Publsiher, vol. 2(9), pages 125-130, September.
    • Handle: RePEc:nap:nijefr:2017:p:125-130
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    References listed on IDEAS

    as
    1. Binh Do & Robert Faff, 2012. "Are Pairs Trading Profits Robust To Trading Costs?," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 35(2), pages 261-287, June.
    2. Erhard Reschenhofer & Thomas Sinkovics, 2017. "Examining the profitability of automatic trading strategies with a focus on trend indicators," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 979-991, July.
    3. Marsaglia, George, 2006. "Ratios of Normal Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 16(i04).
    4. Ryszard Zielinski, 1999. "A Median‐Unbiased Estimator of the AR(1) Coefficient," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(4), pages 477-481, July.
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    Cited by:

    1. Erhard Reschenhofer & Manveer Kaur Mangat & Christian Zwatz & Sándor Guzmics, 2020. "Evaluation of current research on stock return predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 334-351, March.

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